EU Long-term Dataset with Multiple Sensors for Autonomous Driving (2018)
 : Connaissance et Intelligence Artificielle Distribuées (UR 7533) (Université de technologie de Belfort Montbéliard)
 : University of Sheffield - Department of Computer Sciences
 : Czech Technical University in Prague
This dataset was collected with our robocar (in human driving mode of course), equipped with eleven heterogeneous sensors, in the downtown (for long-term data) and suburban (for roundabout data) areas of Montbéliard in France.
Data acquisition date : from May 2018 to Apr 2019
Data acquisition methods :
- Observational data : The vehicle speed was limited to 50 km/h following the French traffic rules. For the long-term data, the driving distance is about 5.0 km (containing a small and a big road loop for loop-closure purpose) and the length of recorded data is about 16 minutes for each collection round. For the roundabout data, the driving distance is about 4.2 km (containing 10 roundabouts with various sizes) and the length of recorded data is about 12 minutes for each collection round.
Formats : application/x-ros-bag
Audience : Research, Stakeholder
- EU Long-term Dataset with Multiple Sensors for Autonomous Driving (https://arxiv.org/pdf/1909.03330.pdf)
Project and funder :
- BQR UTBM 2017-2018
- UTBM (BQR)
Additional information :
As we take privacy very seriously and handle personal data in line with the EU’s data protection law (i.e. the General Data Protection Regulation (GDPR)), we used deep learning-based methods to post-process the camera-recorded images in order to blur face and license plate information. The images have been released successively from the first quarter of 2020.
Record created 23 Jul 2020 by Zhi Yan.
Last modification : 18 Aug 2020.
Local identifier: FR-13002091000019-2020-07-23.